Optical identification method of space typical targets based on combined multi-feature metrics

This paper proposes a new method based on combined multi-feature metrics in order to meet the requirements for identification of satellite local targets. Firstly, we analyze the local physical characteristic of satellite, and construct the local component fractal clustering parameter sets of the fusion morphology multi-feature metrics. Then, a component-clustering model is formulated based on weighted combination of clustering features. Consequently, an optical identification algorithm is proposed to deal with the problems of image degradation and partial occlusion in optical imaging of space targets. Based on the recognition probability in its practical application, the efficiency and robustness of the recognition algorithm is improved by using optimal weighting coefficients in the iteration of particle swarm optimization algorithm. Finally, using four typical satellites and Galileo satellite scaled model, the performance of the identification algorithm is analyzed and verified. Experimental results show that the algorithm is able to identify the satellite component and the identification probability is no less than 0.95, in the case of low contrast, SNR being 5 and severe deformation and mutual occlusion of the components.